746 research outputs found
Computing Semantic Representation: Towards ACG Abstract Terms as Derivation Trees
International audienceThis paper proposes a process to build semantic representation for Tree Adjoining Grammars (TAGs) analysis. Being in the derivation tree tradition, it proposes to reconsider derivation trees as abstract terms (lambda-terms) of Abstract Categorial Grammars (ACGs). The latter offers a flexible tool for expliciting compositionality and semantic combination. The chosen semantic representation language here is an underspecified one. The ACG framework allows to deal both with the semantic language and the derived tree language in an equivalent way: as concrete realizations of the abstract terms. Then, in the semantic part, we can model linguistic phenomena usually considered as difficult for the derivation tree approach
Principles and Implementation of Deductive Parsing
We present a system for generating parsers based directly on the metaphor of
parsing as deduction. Parsing algorithms can be represented directly as
deduction systems, and a single deduction engine can interpret such deduction
systems so as to implement the corresponding parser. The method generalizes
easily to parsers for augmented phrase structure formalisms, such as
definite-clause grammars and other logic grammar formalisms, and has been used
for rapid prototyping of parsing algorithms for a variety of formalisms
including variants of tree-adjoining grammars, categorial grammars, and
lexicalized context-free grammars.Comment: 69 pages, includes full Prolog cod
Interaction Grammars
Interaction Grammar (IG) is a grammatical formalism based on the notion of
polarity. Polarities express the resource sensitivity of natural languages by
modelling the distinction between saturated and unsaturated syntactic
structures. Syntactic composition is represented as a chemical reaction guided
by the saturation of polarities. It is expressed in a model-theoretic framework
where grammars are constraint systems using the notion of tree description and
parsing appears as a process of building tree description models satisfying
criteria of saturation and minimality
Some Novel Applications of Explanation-Based Learning to Parsing Lexicalized Tree-Adjoining Grammars
In this paper we present some novel applications of Explanation-Based
Learning (EBL) technique to parsing Lexicalized Tree-Adjoining grammars. The
novel aspects are (a) immediate generalization of parses in the training set,
(b) generalization over recursive structures and (c) representation of
generalized parses as Finite State Transducers. A highly impoverished parser
called a ``stapler'' has also been introduced. We present experimental results
using EBL for different corpora and architectures to show the effectiveness of
our approach.Comment: uuencoded postscript fil
Incremental Interpretation: Applications, Theory, and Relationship to Dynamic Semantics
Why should computers interpret language incrementally? In recent years
psycholinguistic evidence for incremental interpretation has become more and
more compelling, suggesting that humans perform semantic interpretation before
constituent boundaries, possibly word by word. However, possible computational
applications have received less attention. In this paper we consider various
potential applications, in particular graphical interaction and dialogue. We
then review the theoretical and computational tools available for mapping from
fragments of sentences to fully scoped semantic representations. Finally, we
tease apart the relationship between dynamic semantics and incremental
interpretation.Comment: Procs. of COLING 94, LaTeX (2.09 preferred), 8 page
A Type-Theoretic Account of Neg-Raising Predicates in Tree Adjoining Grammars
International audienceNeg-Raising (NR) verbs form a class of verbs with a clausal complement that show the following behavior: when a negation syntactically attaches to the matrix predicate, it can semantically attach to the embedded predicate. This paper presents an account of NR predicates within Tree Adjoining Grammar (TAG). We propose a lexical semantic interpretation that heavily relies on a Montague-like semantics for TAG and on higher-order types
Grammar induction for mildly context sensitive languages using variational Bayesian inference
The following technical report presents a formal approach to probabilistic
minimalist grammar induction. We describe a formalization of a minimalist
grammar. Based on this grammar, we define a generative model for minimalist
derivations. We then present a generalized algorithm for the application of
variational Bayesian inference to lexicalized mildly context sensitive language
grammars which in this paper is applied to the previously defined minimalist
grammar
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